Junggi Yang

Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance

This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM). WT, PSR, ED, and statistical methods that include frequency distributions and variation, were implemented to extract 24 initial features to use as inputs. Of the 24 initial features, 4 minimum features with the highest accuracy were selected using a non-overlap area distribution measurement method supported by the NEWFM. These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity, specificity, and accuracy of 96.33%, 100%, and 98.17%, respectively. In addition, the area under Receiver Operating Characteristic (ROC) curve was used to measure the performances of NEWFM both without and with feature selections.

Evolutionary rule decision using similarity based associative chronic disease patients

Efficient healthcare management has increasingly drawn much attention in healthcare sector along with recent advances in IT convergence technology. Population aging and a shift from an acute to a chronic disease with a long duration of illness have urgently necessitated healthcare service for efficient, systematic health management. Clinical decision support system (CDSS) is an integrated healthcare system that effectively guides health management and promotion, recommendation for regular health check-up, tailor-made diet therapy, health behavior change for self-care, alert service for drug interaction in patients with chronic diseases with a high prevalence. Although CDSS rule-based algorithm aids guidelines and decision making according to a single chronic disease, it is unable to inform unique characteristics of each chronic disease and suggest preventive strategies and guidelines of complex diseases. Therefore, this study proposes evolutionary rule decision making using similarity based associative chronic disease patients to normalize clinical conditions by utilizing information of each patient and recommend guidelines corresponding detailed conditions in CDSS rule-based inference. Decision making guidelines of chronic disease patients could be systematically established according to various environmental conditions using database of patients with different chronic diseases.

Healthcare Decision Support System for Administration of Chronic Diseases

Objectives A healthcare decision-making support model and rule management system is proposed based on a personalized rule-based intelligent concept, to effectively manage chronic diseases. Methods A Web service was built using a standard message transfer protocol for interoperability of personal health records among healthcare institutions. An intelligent decision service is provided that analyzes data using a service-oriented healthcare rule inference function and machine-learning platform; the rules are extensively compiled by physicians through a developmental user interface that enables knowledge base construction, modification, and integration. Further, screening results are visualized for the self-intuitive understanding of personal health status by patients. Results A recommendation message is output through the Web service by receiving patient information from the hospital information recording system and object attribute values as input factors. The proposed system can verify patient behavior by acting as an intellectualized backbone of chronic diseases management; further, it supports self-management and scheduling of screening. Conclusions Chronic patients can continuously receive active recommendations related to their healthcare through the rule management system, and they can model the system by acting as decision makers in diseases management; secondary diseases can be prevented and health management can be performed by reference to patient-specific lifestyle guidelines.

Clinical decision support system in medical knowledge literature review

The current study involved methodology and content analyses of abstracts of 30 clinical decision support system (CDSS) related studies with high impact factors. The main aim of the current work was to identify the performance and efficiency of CDSS, and enhance the understanding of CDSS for a better health management among the physicians and the patients. To add structure to the current study, major research areas were categorized based on a multidimensional unfolding analysis. In this regard, eight studies were conducted based on theoretical research, ten studies were related to the system and performance of CDSS, and 12 studies verified the efficacy through analysis and evaluation of CDSS. The results indicated that the above-mentioned studies on improvement in systematic performance. Then, based on the improvement, effectively used evaluations were conducted comparably. Moreover, 14 studies analyzed patients’ data and assessed decision support system (DSS). The related findings denoted that DSS has been mainly used for patient management and a large number of studies have verified its effectiveness, using several data to ensure its accuracy and reliability. In addition, the analyzed results of the abstracts and the titles were compared to find whether the titles of the literature articles reveal their content. Using these methodological studies, the academic outlook of medical informatics could be forecasted and the academic quality could be improved by resolving the problems, arising out of system development and realization processes. Such problems can be solved through analyses and interpretation of multilateral parameters, such as the trend in academic development, research direction, topics and methods.

Korean Anaphora Recognition System to Develop Healthcare Dialogue-Type Agent

Objectives Anaphora recognition is a process to identify exactly which noun has been used previously and relates to a pronoun that is included in a specific sentence later. Therefore, anaphora recognition is an essential element of a dialogue agent system. In the current study, all the merits of rule-based, machine learning-based, semantic-based anaphora recognition systems were combined to design and realize a new hybrid-type anaphora recognition system with an optimum capacity. Methods Anaphora recognition rules were encoded on the basis of the internal traits of referred expressions and adjacent contexts to realize a rule-based system and to serve as a baseline. A semantic database, related to predicate instances of sentences including referred expressions, was constructed to identify semantic co-relationships between the referent candidates (to which semantic tags were attached) and the semantic information of predicates. This approach would upgrade the anaphora recognition system by reducing the number of referent candidates. Additionally, to realize a machine learning-based system, an anaphora recognition model was developed on the basis of training data, which indicated referred expressions and referents. The three methods were further combined to develop a new single hybrid-based anaphora recognition system. Results The precision rate of the rule-based systems was 54.9%. However, the precision rate of the hybrid-based system was 63.7%, proving it to be the most efficient method. Conclusions The hybrid-based method, developed by the combination of rule-based and machine learning-based methods, represents a new system with enhanced functional capabilities as compared to other pre-existing individual methods.

A change of the public's emotion depending on Temperature & Humidity index

Abstract Many researches about the effect on politics, economics and Sociocultural phenomenon using the social media are in progress. Authors utilized NAVER Trend most famous web browsing service in korea, NAVER Blog social media, NAVER Cafe service and Open Data(API) and also used temperature, humidity index data of Korea Meteorological Administration. This study analyzed a change of the publics emotion in korea using Cluster analysis of vocabulary of taste among its of feelings and senses. K-means clustering was followed by decision of the number of groups which was used Chi-square goodness of fit test and ward analysis. Eight groups was made and it represented sensitive vocabulary. By Discriminant analysis, eight groups decided by Cluster analysis has 98.9% accuracy. The change of the publics emotion has capability to predict peoples activity so they can share sensibility and a bond of sympathy developed between them. Key Words : Social Media, Social Networking Service, Social Data, Open API, Open Data

A study on influence factors of quality of life and health behavior of cancer patients for the PHR service

Abstract Advancing health screening and treatment of cancer techniques, they contribute to grow the probability of survival of cancer patients for a long time. So health behavior and quality of life of the patients are getting important. This study analysed correlation between healthy person and cancer patients EQ-5D index, social demographic characteristics, health behavior and so on by the multiple regression analysis. The result was that EQ-5D index of cancer patients is lower than healthy persons. Patients of cervical cancer and lung cancer had very low the index especially. In conclusion, cancer have a bad influence on the quality of life. For cancer patients, smoking and drinking are a major factors of correlation. The number of non-smokers among the patients is lower than the number of smokers among healthy persons. This conclusion means that the importance of health behaviors and quality of life for cancer patients is established so that this will be used for basic reference of PHR models and service enhancing quality of life.